Competitive Influence Maximization Across Social Networks

Ruisi Yang, Chunfen Bu, Qiang Yue, Xing Jiang, Qiangnan Ma, Yunfei Zhang
Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, PMLR 278:511-518, 2025.

Abstract

The proliferation of Web 2.0 technologies has significantly reshaped information propagation dynamics across social media platforms. While existing studies extensively analyze influence maximization within single-platform environments, competitive propagation dynamics across multiple interconnected social networks remain underexplored. Addressing this research gap, we define the Competitive Influence Maximization Across Social Networks (CIMASN) problem and introduce a novel Competitive Independent Cascade Model (CICM) that incorporates competitive influences propagating simultaneously across multiple platforms. A greedy algorithm is proposed for effective seed node selection under this competitive scenario, validated through extensive experiments on both real-world and synthetic datasets. Results demonstrate that our model and algorithm significantly outperform traditional approaches, highlighting the necessity and effectiveness of modeling competitive propagation dynamics across multiple social networks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v278-yang25e, title = {Competitive Influence Maximization Across Social Networks}, author = {Yang, Ruisi and Bu, Chunfen and Yue, Qiang and Jiang, Xing and Ma, Qiangnan and Zhang, Yunfei}, booktitle = {Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing}, pages = {511--518}, year = {2025}, editor = {Zeng, Nianyin and Pachori, Ram Bilas and Wang, Dongshu}, volume = {278}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v278/main/assets/yang25e/yang25e.pdf}, url = {https://proceedings.mlr.press/v278/yang25e.html}, abstract = { The proliferation of Web 2.0 technologies has significantly reshaped information propagation dynamics across social media platforms. While existing studies extensively analyze influence maximization within single-platform environments, competitive propagation dynamics across multiple interconnected social networks remain underexplored. Addressing this research gap, we define the Competitive Influence Maximization Across Social Networks (CIMASN) problem and introduce a novel Competitive Independent Cascade Model (CICM) that incorporates competitive influences propagating simultaneously across multiple platforms. A greedy algorithm is proposed for effective seed node selection under this competitive scenario, validated through extensive experiments on both real-world and synthetic datasets. Results demonstrate that our model and algorithm significantly outperform traditional approaches, highlighting the necessity and effectiveness of modeling competitive propagation dynamics across multiple social networks.} }
Endnote
%0 Conference Paper %T Competitive Influence Maximization Across Social Networks %A Ruisi Yang %A Chunfen Bu %A Qiang Yue %A Xing Jiang %A Qiangnan Ma %A Yunfei Zhang %B Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2025 %E Nianyin Zeng %E Ram Bilas Pachori %E Dongshu Wang %F pmlr-v278-yang25e %I PMLR %P 511--518 %U https://proceedings.mlr.press/v278/yang25e.html %V 278 %X The proliferation of Web 2.0 technologies has significantly reshaped information propagation dynamics across social media platforms. While existing studies extensively analyze influence maximization within single-platform environments, competitive propagation dynamics across multiple interconnected social networks remain underexplored. Addressing this research gap, we define the Competitive Influence Maximization Across Social Networks (CIMASN) problem and introduce a novel Competitive Independent Cascade Model (CICM) that incorporates competitive influences propagating simultaneously across multiple platforms. A greedy algorithm is proposed for effective seed node selection under this competitive scenario, validated through extensive experiments on both real-world and synthetic datasets. Results demonstrate that our model and algorithm significantly outperform traditional approaches, highlighting the necessity and effectiveness of modeling competitive propagation dynamics across multiple social networks.
APA
Yang, R., Bu, C., Yue, Q., Jiang, X., Ma, Q. & Zhang, Y.. (2025). Competitive Influence Maximization Across Social Networks. Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 278:511-518 Available from https://proceedings.mlr.press/v278/yang25e.html.

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